Diagnose Decline in Successful Orders

You are a Data Scientist at a food-delivery marketplace. In one geographic market, the number of **successful orders** has declined over the past 4 weeks. Assume a **successful order** is an order that is placed, accepted, delivered, and not later refunded or canceled due to marketplace failures. The business wants to know: 1. **What could be causing the decline?** 2. **What metrics and slices would you examine first?** 3. **How would you distinguish between demand-side, supply-side, operational, and measurement issues?** 4. **What analyses and A/B tests would you propose to validate root causes and improve the metric?** Please structure your answer as if you were leading the investigation for a marketplace product team. You may assume the platform has the following relevant data available: - Customer sessions / app opens - Search and menu views - Add-to-cart and checkout events - Order placement events - Merchant acceptance / rejection - Dasher assignment and delivery times - Cancellations, refunds, and support contacts - Pricing, fees, promos, ETAs, and stockout signals - Customer, merchant, and dasher attributes by market and time In your answer, define a metric framework for diagnosing the drop. Consider factors such as: - Seasonality, holidays, weather, outages, and local events - Changes in demand, conversion, merchant availability, courier supply, ETAs, pricing, and service quality - Mix shift across customer segments, cuisines, merchants, or time of day - Data quality / instrumentation issues - Short-term fixes versus longer-term product interventions Finally, describe one or two experiments you would run, including: - Primary metric - Guardrail metrics - Likely sources of bias or confounding - How you would interpret ambiguous results